In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import spotipy
import os
%matplotlib inline
In [2]:
spotify_data = pd.read_csv('C:\\Users\\RS\\data.csv')
genre_data = pd.read_csv('C:\\Users\\RS\\data_by_genres.csv')
data_by_year = pd.read_csv('C:\\Users\\RS\\data_by_year.csv')
In [3]:
spotify_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 170653 entries, 0 to 170652
Data columns (total 19 columns):
 #   Column            Non-Null Count   Dtype  
---  ------            --------------   -----  
 0   valence           170653 non-null  float64
 1   year              170653 non-null  int64  
 2   acousticness      170653 non-null  float64
 3   artists           170653 non-null  object 
 4   danceability      170653 non-null  float64
 5   duration_ms       170653 non-null  int64  
 6   energy            170653 non-null  float64
 7   explicit          170653 non-null  int64  
 8   id                170653 non-null  object 
 9   instrumentalness  170653 non-null  float64
 10  key               170653 non-null  int64  
 11  liveness          170653 non-null  float64
 12  loudness          170653 non-null  float64
 13  mode              170653 non-null  int64  
 14  name              170653 non-null  object 
 15  popularity        170653 non-null  int64  
 16  release_date      170653 non-null  object 
 17  speechiness       170653 non-null  float64
 18  tempo             170653 non-null  float64
dtypes: float64(9), int64(6), object(4)
memory usage: 24.7+ MB
In [4]:
genre_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2973 entries, 0 to 2972
Data columns (total 14 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   mode              2973 non-null   int64  
 1   genres            2973 non-null   object 
 2   acousticness      2973 non-null   float64
 3   danceability      2973 non-null   float64
 4   duration_ms       2973 non-null   float64
 5   energy            2973 non-null   float64
 6   instrumentalness  2973 non-null   float64
 7   liveness          2973 non-null   float64
 8   loudness          2973 non-null   float64
 9   speechiness       2973 non-null   float64
 10  tempo             2973 non-null   float64
 11  valence           2973 non-null   float64
 12  popularity        2973 non-null   float64
 13  key               2973 non-null   int64  
dtypes: float64(11), int64(2), object(1)
memory usage: 325.3+ KB
In [5]:
data_by_year.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 14 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   mode              100 non-null    int64  
 1   year              100 non-null    int64  
 2   acousticness      100 non-null    float64
 3   danceability      100 non-null    float64
 4   duration_ms       100 non-null    float64
 5   energy            100 non-null    float64
 6   instrumentalness  100 non-null    float64
 7   liveness          100 non-null    float64
 8   loudness          100 non-null    float64
 9   speechiness       100 non-null    float64
 10  tempo             100 non-null    float64
 11  valence           100 non-null    float64
 12  popularity        100 non-null    float64
 13  key               100 non-null    int64  
dtypes: float64(11), int64(3)
memory usage: 11.1 KB

Exploratory Data Analysis¶

In [6]:
import plotly.express as px
In [7]:
sound_features = ['acousticness', 'danceability', 'energy',
                  'instrumentalness', 'liveness', 'valence']
fig = px.line(data_by_year, x='year', y=sound_features)
fig.show()
In [8]:
fig = px.line(data_by_year, x='year', y='tempo')
fig.show()
In [9]:
# Boxplot of danceability by genre
sns.boxplot(x='danceability', y='genres', data=genre_data)
plt.title("Danceability by Genre")
plt.show()
In [10]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# assume genre_data is a pandas DataFrame containing the relevant data

# calculate mean danceability for each genre
genre_danceability = genre_data.groupby('genres')['danceability'].mean()

# sort genres by danceability and select top 10
top_genres = genre_danceability.sort_values(ascending=False).head(10).index.tolist()

# filter the data for only the top 10 genres
genre_data_top10 = genre_data[genre_data['genres'].isin(top_genres)]

# create the histogram
sns.histplot(x='danceability', hue='genres', data=genre_data_top10, multiple='stack')
plt.title("Danceability by Top 10 Genres")
plt.show()
In [34]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# assume genre_data is a pandas DataFrame containing the relevant data

# calculate mean danceability for each genre
genre_danceability = genre_data.groupby('genres')['danceability'].mean()

# sort genres by danceability and select top 10
top_genres = genre_danceability.sort_values(ascending=False).head(10)

# create a color palette
palette = sns.color_palette("hls", 10)

# create the bar plot with a different color for each bar
sns.barplot(x=top_genres.values, y=top_genres.index, palette=palette)
plt.title("Top 10 Genres by Mean Danceability")
plt.xlabel("Mean Danceability")
plt.ylabel("Genre")
plt.show()

Characteristics of Differenct Genres¶

In [13]:
top_10_genres = genre_data.nlargest(10, 'popularity')
fig = px.bar(top_10_genres, x='genres', y=['valence', 'energy',
                                           'danceability', 'acousticness'],
            barmode='group')
fig.show()

Clustering Genres with K-Means¶

In [14]:
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

cluster_pipeline = Pipeline([('scaler', StandardScaler()),
                            ('kmeans', KMeans(n_clusters=10))])
X = genre_data.select_dtypes(np.number)
cluster_pipeline.fit(X)
genre_data['cluster'] = cluster_pipeline.predict(X)

Visualizing the Genre Clusters with t-SNE¶

In [15]:
from sklearn.manifold import TSNE
In [16]:
tsne_pipeline = Pipeline([('scaler', StandardScaler()),
                         ('tsne', TSNE(n_components=2, verbose=2))])
genre_embedding = tsne_pipeline.fit_transform(X)
projection = pd.DataFrame(columns=['x', 'y'], data=genre_embedding)
projection['genres'] = genre_data['genres']
projection['cluster'] = genre_data['cluster']

D:\G\ss\lib\site-packages\sklearn\manifold\_t_sne.py:780: FutureWarning:

The default initialization in TSNE will change from 'random' to 'pca' in 1.2.

D:\G\ss\lib\site-packages\sklearn\manifold\_t_sne.py:790: FutureWarning:

The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.


[t-SNE] Computing 91 nearest neighbors...
[t-SNE] Indexed 2973 samples in 0.148s...
[t-SNE] Computed neighbors for 2973 samples in 0.373s...
[t-SNE] Computed conditional probabilities for sample 1000 / 2973
[t-SNE] Computed conditional probabilities for sample 2000 / 2973
[t-SNE] Computed conditional probabilities for sample 2973 / 2973
[t-SNE] Mean sigma: 0.777516
[t-SNE] Computed conditional probabilities in 0.066s
[t-SNE] Iteration 50: error = 81.9775620, gradient norm = 0.0043106 (50 iterations in 0.903s)
[t-SNE] Iteration 100: error = 76.7715149, gradient norm = 0.0183697 (50 iterations in 0.759s)
[t-SNE] Iteration 150: error = 76.2398071, gradient norm = 0.0047412 (50 iterations in 0.603s)
[t-SNE] Iteration 200: error = 76.1542206, gradient norm = 0.0007833 (50 iterations in 0.941s)
[t-SNE] Iteration 250: error = 76.1253738, gradient norm = 0.0004384 (50 iterations in 0.986s)
[t-SNE] KL divergence after 250 iterations with early exaggeration: 76.125374
[t-SNE] Iteration 300: error = 1.8275498, gradient norm = 0.0011280 (50 iterations in 0.976s)
[t-SNE] Iteration 350: error = 1.5905074, gradient norm = 0.0003895 (50 iterations in 0.867s)
[t-SNE] Iteration 400: error = 1.5003856, gradient norm = 0.0002519 (50 iterations in 0.975s)
[t-SNE] Iteration 450: error = 1.4568652, gradient norm = 0.0001596 (50 iterations in 0.893s)
[t-SNE] Iteration 500: error = 1.4333408, gradient norm = 0.0001210 (50 iterations in 0.905s)
[t-SNE] Iteration 550: error = 1.4192455, gradient norm = 0.0000979 (50 iterations in 0.907s)
[t-SNE] Iteration 600: error = 1.4100113, gradient norm = 0.0001056 (50 iterations in 0.926s)
[t-SNE] Iteration 650: error = 1.4039299, gradient norm = 0.0000735 (50 iterations in 0.884s)
[t-SNE] Iteration 700: error = 1.3995304, gradient norm = 0.0000698 (50 iterations in 0.901s)
[t-SNE] Iteration 750: error = 1.3961930, gradient norm = 0.0000670 (50 iterations in 0.932s)
[t-SNE] Iteration 800: error = 1.3934026, gradient norm = 0.0000620 (50 iterations in 0.933s)
[t-SNE] Iteration 850: error = 1.3909042, gradient norm = 0.0000536 (50 iterations in 0.960s)
[t-SNE] Iteration 900: error = 1.3887153, gradient norm = 0.0000481 (50 iterations in 0.864s)
[t-SNE] Iteration 950: error = 1.3869228, gradient norm = 0.0000530 (50 iterations in 0.987s)
[t-SNE] Iteration 1000: error = 1.3850130, gradient norm = 0.0000471 (50 iterations in 0.870s)
[t-SNE] KL divergence after 1000 iterations: 1.385013
In [17]:
import plotly.express as px
fig = px.scatter(
    projection, x='x', y='y', color='cluster', hover_data=['x', 'y',
                                                           'genres']
)
fig.show()
In [18]:
genre_data
Out[18]:
mode genres acousticness danceability duration_ms energy instrumentalness liveness loudness speechiness tempo valence popularity key cluster
0 1 21st century classical 0.979333 0.162883 1.602977e+05 0.071317 0.606834 0.361600 -31.514333 0.040567 75.336500 0.103783 27.833333 6 2
1 1 432hz 0.494780 0.299333 1.048887e+06 0.450678 0.477762 0.131000 -16.854000 0.076817 120.285667 0.221750 52.500000 5 1
2 1 8-bit 0.762000 0.712000 1.151770e+05 0.818000 0.876000 0.126000 -9.180000 0.047000 133.444000 0.975000 48.000000 7 8
3 1 [] 0.651417 0.529093 2.328809e+05 0.419146 0.205309 0.218696 -12.288965 0.107872 112.857352 0.513604 20.859882 7 4
4 1 a cappella 0.676557 0.538961 1.906285e+05 0.316434 0.003003 0.172254 -12.479387 0.082851 112.110362 0.448249 45.820071 7 6
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2968 1 zolo 0.222625 0.547082 2.580991e+05 0.610240 0.143872 0.204206 -11.295878 0.061088 125.494919 0.596155 33.778943 9 6
2969 0 zouglou 0.161000 0.863000 2.063200e+05 0.909000 0.000000 0.108000 -5.985000 0.081300 119.038000 0.845000 58.000000 7 7
2970 1 zouk 0.263261 0.748889 3.060728e+05 0.622444 0.257227 0.089678 -10.289222 0.038778 101.965222 0.824111 46.666667 5 8
2971 0 zurich indie 0.993000 0.705667 1.984173e+05 0.172667 0.468633 0.179667 -11.453333 0.348667 91.278000 0.739000 0.000000 7 4
2972 1 zydeco 0.421038 0.629409 1.716717e+05 0.609369 0.019248 0.255877 -9.854825 0.050491 126.366087 0.808544 30.261905 7 8

2973 rows × 15 columns

Clustering songs with K-Means¶

In [19]:
song_cluster_pipeline = Pipeline([('scaler', StandardScaler()),
                                 ('kmeans', KMeans(n_clusters=20,
                                                   verbose=2))],
                                verbose=True)
X = spotify_data.select_dtypes(np.number)
number_cols = list(X.columns)
number_cols
song_cluster_pipeline.fit(X)
song_cluster_labels = song_cluster_pipeline.predict(X)
spotify_data['cluster_label'] = song_cluster_labels

[Pipeline] ............ (step 1 of 2) Processing scaler, total=   0.1s
Initialization complete
Iteration 0, inertia 1518431.2107395953
Iteration 1, inertia 1176719.0925323036
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Iteration 27, inertia 1058044.0468948237
Converged at iteration 27: center shift 9.90817964533811e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1559384.1776030476
Iteration 1, inertia 1195634.6516553042
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Converged at iteration 78: center shift 8.294355896458048e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1481004.6992672693
Iteration 1, inertia 1151723.5111618347
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Converged at iteration 61: center shift 9.561298688633168e-05 within tolerance 0.00010000000000000789.
Initialization complete
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Converged at iteration 74: center shift 8.717316594747099e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1528040.2246529965
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Iteration 85, inertia 1058926.473769887
Converged at iteration 85: center shift 9.4595908616319e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1543090.5000349204
Iteration 1, inertia 1166324.3616256032
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Converged at iteration 31: center shift 9.386443328024657e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1527430.9989983155
Iteration 1, inertia 1189477.661161892
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Converged at iteration 27: center shift 6.317482042283155e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1530010.1767161565
Iteration 1, inertia 1159639.7293103684
Iteration 2, inertia 1112482.6460443856
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Iteration 63, inertia 1061166.0375455136
Converged at iteration 63: center shift 9.567525712380937e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1485366.34937874
Iteration 1, inertia 1166914.313650583
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Converged at iteration 133: center shift 9.970076741224624e-05 within tolerance 0.00010000000000000789.
Initialization complete
Iteration 0, inertia 1533726.2830152828
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Iteration 2, inertia 1099930.2580672416
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Converged at iteration 37: center shift 7.546514179991207e-05 within tolerance 0.00010000000000000789.
[Pipeline] ............ (step 2 of 2) Processing kmeans, total=  25.6s
In [20]:
spotify_data
Out[20]:
valence year acousticness artists danceability duration_ms energy explicit id instrumentalness key liveness loudness mode name popularity release_date speechiness tempo cluster_label
0 0.0594 1921 0.98200 ['Sergei Rachmaninoff', 'James Levine', 'Berli... 0.279 831667 0.211 0 4BJqT0PrAfrxzMOxytFOIz 0.878000 10 0.6650 -20.096 1 Piano Concerto No. 3 in D Minor, Op. 30: III. ... 4 1921 0.0366 80.954 12
1 0.9630 1921 0.73200 ['Dennis Day'] 0.819 180533 0.341 0 7xPhfUan2yNtyFG0cUWkt8 0.000000 7 0.1600 -12.441 1 Clancy Lowered the Boom 5 1921 0.4150 60.936 11
2 0.0394 1921 0.96100 ['KHP Kridhamardawa Karaton Ngayogyakarta Hadi... 0.328 500062 0.166 0 1o6I8BglA6ylDMrIELygv1 0.913000 3 0.1010 -14.850 1 Gati Bali 5 1921 0.0339 110.339 3
3 0.1650 1921 0.96700 ['Frank Parker'] 0.275 210000 0.309 0 3ftBPsC5vPBKxYSee08FDH 0.000028 5 0.3810 -9.316 1 Danny Boy 3 1921 0.0354 100.109 17
4 0.2530 1921 0.95700 ['Phil Regan'] 0.418 166693 0.193 0 4d6HGyGT8e121BsdKmw9v6 0.000002 3 0.2290 -10.096 1 When Irish Eyes Are Smiling 2 1921 0.0380 101.665 17
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
170648 0.6080 2020 0.08460 ['Anuel AA', 'Daddy Yankee', 'KAROL G', 'Ozuna... 0.786 301714 0.808 0 0KkIkfsLEJbrcIhYsCL7L5 0.000289 7 0.0822 -3.702 1 China 72 2020-05-29 0.0881 105.029 0
170649 0.7340 2020 0.20600 ['Ashnikko'] 0.717 150654 0.753 0 0OStKKAuXlxA0fMH54Qs6E 0.000000 7 0.1010 -6.020 1 Halloweenie III: Seven Days 68 2020-10-23 0.0605 137.936 4
170650 0.6370 2020 0.10100 ['MAMAMOO'] 0.634 211280 0.858 0 4BZXVFYCb76Q0Klojq4piV 0.000009 4 0.2580 -2.226 0 AYA 76 2020-11-03 0.0809 91.688 16
170651 0.1950 2020 0.00998 ['Eminem'] 0.671 337147 0.623 1 5SiZJoLXp3WOl3J4C8IK0d 0.000008 2 0.6430 -7.161 1 Darkness 70 2020-01-17 0.3080 75.055 14
170652 0.6420 2020 0.13200 ['KEVVO', 'J Balvin'] 0.856 189507 0.721 1 7HmnJHfs0BkFzX4x8j0hkl 0.004710 7 0.1820 -4.928 1 Billetes Azules (with J Balvin) 74 2020-10-16 0.1080 94.991 14

170653 rows × 20 columns

Visualizing the Song Clusters with PCA¶

In [21]:
from sklearn.decomposition import PCA
pca_pipeline = Pipeline([('scaler', StandardScaler()),
                        ('PCA', PCA(n_components=2))])
song_embedding = pca_pipeline.fit_transform(X)
projection = pd.DataFrame(columns=['x', 'y'], data=song_embedding)
projection['title'] = spotify_data['name']
projection['cluster'] = spotify_data['cluster_label']
In [22]:
import plotly.express as px
fig = px.scatter(projection, x='x', y='y', color='cluster',
                hover_data=['x', 'y', 'title'])
fig.show()
In [23]:
from spotipy.oauth2 import SpotifyClientCredentials
from collections import defaultdict
In [24]:
sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(
    client_id='2a1633b8d0e94800b7a7707870dad747',
    client_secret='8e416de66f734433ac87282497c1c6b1'
))
In [25]:
def find_song(name, year):
    """
    Returns a dataframe with data for a song given the name and release year.
    Uses Spotify to fetch audio features and metadata for the specified song.
    """
    song_data = defaultdict()
    results = sp.search(q= 'track: {} year: {}'.format(name, year), limit=1)
    
    if not results['tracks']['items']:
        return None
    
    results = results['tracks']['items'][0]
    
    track_id = results['id']
    audio_features = sp.audio_features(track_id)[0]
    song_data['name'] = [name]
    song_data['year'] = [year]
    song_data['explicit'] = [int(results['explicit'])]
    song_data['duration_ms'] = [results['duration_ms']]
    song_data['popularity'] = [results['popularity']]
    
    for key, value in audio_features.items():
        song_data[key] = value
    
    return pd.DataFrame(song_data)
In [26]:
def flatten_dict_list(songs_list):
    flattened_dict = defaultdict()
    
    for key in songs_list[0].keys():
        flattened_dict[key] = []
        
    for dictionary in songs_list:
        for key, value in dictionary.items():
            flattened_dict[key].append(value)
            
    return flattened_dict
In [27]:
def get_song_data(song, spotify_data):
    try:
        song_data = spotify_data[(spotify_data['name'] == song['name'])
                                 & (spotify_data['year'] == song['year'])].iloc[0]
    except IndexError:
        return find_song(song['name'], song['year'])
    return song_data
In [28]:
def get_mean_vector(songs_list, spotify_data):
    """
    Mean vector for a list of songs
    """
    song_vectors = []
    for song in songs_list:
        song_data = get_song_data(song, spotify_data)
        if song_data is None:
            print('Warning: {} does not exist in Spotify or in database'.format(song['name']))
        song_vector = song_data[number_cols].values
        song_vectors.append(song_vector)
    song_matrix = np.array(list(song_vectors))
    return np.mean(song_matrix, axis=0)
In [29]:
from scipy.spatial.distance import cdist

def recommended_songs(songs_list, spotify_data, n_songs=10):
    """
    Recommends songs based on a list of previous songs that a user
    has listened to.
    """
    cols = ['name', 'year', 'artists']
    song_dict = flatten_dict_list(songs_list)
    song_center = get_mean_vector(songs_list, spotify_data)
    scaler = song_cluster_pipeline.steps[0][1]
    scaled_data = scaler.transform(spotify_data[number_cols])
    scaled_song_center = scaler.transform(song_center.reshape(1, -1))
    distances = cdist(scaled_song_center, scaled_data, 'cosine')
    index = list(np.argsort(distances)[:, :n_songs][0])
    rec_songs = spotify_data.iloc[index]
    rec_songs = rec_songs[~rec_songs['name'].isin(song_dict['name'])]
    return rec_songs[cols].to_dict(orient='records')
In [30]:
recommended_songs([
    {'name': 'Come As You Are', 'year':1991},
    {'name': 'Smells Like Teen Spirit', 'year': 1991},
    {'name': 'Lithium', 'year': 1992},
    {'name': 'All Apologies', 'year': 1993},
    {'name': 'Stay Away', 'year': 1993}
],  spotify_data)

C:\Users\RS\AppData\Local\Temp\ipykernel_14464\3647570856.py:12: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

D:\G\ss\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but StandardScaler was fitted with feature names
Out[30]:
[{'name': 'Life is a Highway - From "Cars"',
  'year': 2009,
  'artists': "['Rascal Flatts']"},
 {'name': 'Of Wolf And Man', 'year': 1991, 'artists': "['Metallica']"},
 {'name': 'Somebody Like You', 'year': 2002, 'artists': "['Keith Urban']"},
 {'name': 'Corazón Mágico', 'year': 1995, 'artists': "['Los Fugitivos']"},
 {'name': 'Kayleigh', 'year': 1992, 'artists': "['Marillion']"},
 {'name': 'Little Secrets', 'year': 2009, 'artists': "['Passion Pit']"},
 {'name': 'No Excuses', 'year': 1994, 'artists': "['Alice In Chains']"},
 {'name': "Let's Get Rocked", 'year': 1992, 'artists': "['Def Leppard']"},
 {'name': 'If Today Was Your Last Day',
  'year': 2008,
  'artists': "['Nickelback']"},
 {'name': "Things I'll Never Say",
  'year': 2002,
  'artists': "['Avril Lavigne']"}]
In [31]:
recommended_songs([{'name':'Beat It', 'year': 1982},
                 {'name': 'Billie Jean', 'year': 1988},
                 {'name': 'Thriller', 'year': 1982}], spotify_data)

C:\Users\RS\AppData\Local\Temp\ipykernel_14464\3647570856.py:12: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

D:\G\ss\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but StandardScaler was fitted with feature names
Out[31]:
[{'name': 'Hot Legs', 'year': 1977, 'artists': "['Rod Stewart']"},
 {'name': 'Thriller - 2003 Edit',
  'year': 2003,
  'artists': "['Michael Jackson']"},
 {'name': "I Didn't Mean To Turn You On",
  'year': 1984,
  'artists': "['Cherrelle']"},
 {'name': 'Stars On 45 - Original Single Version',
  'year': 1981,
  'artists': "['Stars On 45']"},
 {'name': "Stars On '89 Remix - Radio Version",
  'year': 1984,
  'artists': "['Stars On 45']"},
 {'name': 'Take Me to the River - Live',
  'year': 1984,
  'artists': "['Talking Heads']"},
 {'name': 'Nothing Can Stop Us', 'year': 1992, 'artists': "['Saint Etienne']"}]
In [32]:
recommended_songs([{'name':'when you say nothing at all', 'year': 1999},], spotify_data)

D:\G\ss\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but StandardScaler was fitted with feature names
Out[32]:
[{'name': 'Tell It Like It Is',
  'year': 1989,
  'artists': "['Wayne Toups', 'Zydecajun']"},
 {'name': 'In a Different Light', 'year': 1990, 'artists': "['Doug Stone']"},
 {'name': 'Born Country', 'year': 1991, 'artists': "['Alabama']"},
 {'name': 'What You Need', 'year': 2014, 'artists': "['Dan Sacks']"},
 {'name': 'Adelante Corazón', 'year': 1989, 'artists': "['Daniela Romo']"},
 {'name': 'Come With Me', 'year': 1995, 'artists': "['Shai']"},
 {'name': 'Shot Full Of Love', 'year': 1991, 'artists': "['Chris LeDoux']"},
 {'name': 'Happy, Happy Birthday Baby',
  'year': 1986,
  'artists': "['Ronnie Milsap']"},
 {'name': 'Too Busy Being In Love', 'year': 1990, 'artists': "['Doug Stone']"},
 {'name': 'Por Una Mujer Bonita',
  'year': 1998,
  'artists': "['Miguel Y Miguel']"}]
In [33]:
recommended_songs([{'name':'rock you like a hurricane', 'year': 1984},], spotify_data)

D:\G\ss\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but StandardScaler was fitted with feature names
Out[33]:
[{'name': 'Rock You Like A Hurricane',
  'year': 1984,
  'artists': "['Scorpions']"},
 {'name': 'Barracuda', 'year': 1977, 'artists': "['Heart']"},
 {'name': 'While My Guitar Gently Weeps - Remastered 2009',
  'year': 1968,
  'artists': "['The Beatles']"},
 {'name': 'No Easy Way Out - From "Rocky IV" Soundtrack',
  'year': 1985,
  'artists': "['Robert Tepper']"},
 {'name': 'Youth Gone Wild', 'year': 1989, 'artists': "['Skid Row']"},
 {'name': 'Woodstock',
  'year': 1970,
  'artists': "['Crosby, Stills, Nash & Young']"},
 {'name': 'Careless Whisper', 'year': 1998, 'artists': "['George Michael']"},
 {'name': 'Jesus Is Just Alright with Me - 2007 Remaster',
  'year': 1976,
  'artists': "['The Doobie Brothers']"},
 {'name': 'No Easy Way Out - From "Rocky IV" Soundtrack',
  'year': 1986,
  'artists': "['Robert Tepper']"},
 {'name': 'Scatman (ski-ba-bop-ba-dop-bop)',
  'year': 1995,
  'artists': "['Scatman John']"}]
In [36]:
recommended_songs([{'name':'chemical. post malone', 'year': 2023},], spotify_data)

D:\G\ss\lib\site-packages\sklearn\base.py:450: UserWarning:

X does not have valid feature names, but StandardScaler was fitted with feature names
Out[36]:
[{'name': 'bloody valentine',
  'year': 2020,
  'artists': "['Machine Gun Kelly']"},
 {'name': 'bloody valentine',
  'year': 2020,
  'artists': "['Machine Gun Kelly']"},
 {'name': 'bad idea!', 'year': 2019, 'artists': "['girl in red']"},
 {'name': 'UCLA', 'year': 2018, 'artists': "['RL Grime', '24hrs']"},
 {'name': 'Who Do You Love',
  'year': 2019,
  'artists': "['The Chainsmokers', '5 Seconds of Summer']"},
 {'name': 'Legends', 'year': 2018, 'artists': "['Juice WRLD']"},
 {'name': 'Cry Alone', 'year': 2018, 'artists': "['Lil Peep']"},
 {'name': 'Feels Great (feat. Fetty Wap & CVBZ)',
  'year': 2017,
  'artists': "['Cheat Codes', 'Fetty Wap', 'CVBZ']"},
 {'name': 'Nightmares (feat. Lil Skies)',
  'year': 2019,
  'artists': "['Yung Pinch', 'Lil Skies']"},
 {'name': 'Man Of The Year', 'year': 2020, 'artists': "['Juice WRLD']"}]